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Showing 721-735 of 1,267 supervisors
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Researcher
Michael Greenberg is a researcher at Stevens Institute of Technology. He actively contributes to programming language design, static analysis, and formal methods, with a focus on Datalog, SMT solvers, and type systems.
Research Professor
Dale Miller is a Research Professor at Inria Saclay , with affiliations at the Laboratoire d'Informatique (LIX) and Institut Polytechnique de Paris . He previously served as a Professor at the University of Pennsylvania, École Polytechnique (France) , and as Department Head at Pennsylvania State University. He holds a Ph.D. in Mathematics from Carnegie Mellon University (1983) , supervised by Peter Andrews. Research interests span computational logic , proof theory , automated reasoning , logic programming , unification , operational semantics , and proof certificates . His work unifies logical foundations with computational applications, particularly in higher-order logic programming and theorem proving. Key contributions include the Abella and Bedwyr theorem provers, λProlog language, and foundational research on proof certificates . He has received the ERC Advanced Grant (2012-2016) , Dov Gabbay Prize (2023) , and LICS Test-of-Time Awards (2011, 2014) . Students advised include Farah Al Wardani , Jui-Hsuan Wu , Matteo Manighetti , and Zakaria Chihani , among others. He leads the Partout team at Inria, focusing on logical frameworks and verification.
Assistant Professor
Lionel Parreaux is an Assistant Professor at The Hong Kong University of Science and Technology (HKUST) in the Department of Computer Science and Engineering since February 2021. He is actively recruiting students for his research group focusing on programming languages, type systems, and compiler optimization. He earned his PhD in 2020 from École polytechnique fédérale de Lausanne (EPFL) under the Data Analysis Theory and Applications Laboratory (DATA), where he developed the Squid type-safe metaprogramming library for Scala. His research spans Programming Languages Type Systems Functional Programming Generative Programming Compiler Optimization Language Design Recent publications (2024) cover type inference, metaprogramming, and compiler optimization. Notable works include Scope-safe metaprogramming Deforestation techniques Conditional syntax design Subtyping constraints Lazy memory management These reflect his focus on theoretical and practical language implementation challenges. Current research projects include "A Lightweight Type System for Scope and Effect Safety" (2024, Huawei Technologies) "Optimizing Functional Programs by Building on Optimal Graph Reduction Techniques" (2022-2025, RGC Early Career Scheme) He supervises 8 PhD/MPhil students and serves on program committees for POPL, APLAS, GPCE, and SPLASH symposia.
Researcher
Martin Tabakow, PhD, is a researcher at the Department of Artificial Intelligence within the Faculty of Information and Communication Technology at Wrocław University of Science and Technology. His work focuses on artificial intelligence applications in medical imaging and computational biology. University: Wrocław University of Science and Technology School: Faculty of Information and Communication Technology Department: Artificial Intelligence Academic Rank: Researcher His research interests include: Deep learning for histopathology and retinal disease detection Fuzzy logic systems and type-2 fuzzy inference Data bias mitigation and class imbalance solutions Medical image segmentation and computational healthcare Publications (2019–2024) demonstrate expertise in using AI for biomedical applications. Notable trends include: Advancements in histopathological image analysis Development of type-2 fuzzy models for improved classification Focus on synthetic data generation and bias reduction Integration of AI with clinical diagnostic systems Current laboratory affiliations include the Artificial Intelligence Department's research teams focused on medical applications.
Researcher
Thanasis Vogogias is a Researcher at the School of Computing , Edinburgh Napier University . His work focuses on the intersection of information visualisation , bioinformatics , and computational biology , with a particular emphasis on developing tools for analyzing complex biological networks and datasets. Research Interests include: Network analysis with multiple edge types Bayesian network structure learning Hierarchical clustering and dendrogram visualization Genomic data processing and QTL mapping Integration of computational methods with biological applications Recent publications highlight his work in: Visual encodings for network representation Bayesian inference in biological systems Multi-height branch-cut techniques for gene expression data Software development for autotetraploid population analysis The BayesPiles tool exemplifies his contributions to visual analytics for consensus network construction.
Associate Professor
Hitomi Yanaka is an Associate Professor (tenured) at the University of Tokyo and Team Leader of the Explainable AI Team at RIKEN. Her research focuses on Natural Language Processing (NLP), Artificial Intelligence, and formal semantics, with a specialization in logical inference systems and compositional semantics. She holds a Ph.D. in Engineering from the University of Tokyo and has been recognized with prestigious awards including the Young Scientists Award from the Ministry of Education and Forbes JAPAN's Women In Tech TOP30 in 2024. Her academic roles include leadership in the "覚醒" project and affiliations with ACL, JSAI, and ANLP. Yanaka's work bridges theoretical linguistics and computational methods, contributing to semantic analysis, bias detection in LLMs, and multimodal reasoning. She teaches courses on computational linguistics and logic at the University of Tokyo, and her research spans over 150 publications in top-tier conferences like ACL, NLP, and Cognitive Science Society. Award highlights include the 2024 文部科学大臣表彰 (Ministry of Education Award), 2023船井研究奨励賞, and multiple best paper awards. Her lab, Yanaka Laboratory, focuses on explainable AI, ethical AI, and advancing NLP through logical frameworks. Current projects include analyzing social biases in Japanese LLMs and developing neuro-symbolic systems for multimodal tasks.
Professor
Bjarne Stroustrup is a Professor of Computer Science at Columbia University in New York City. He is renowned as the designer and original implementor of the C++ programming language. His work has profoundly influenced software development, emphasizing object-oriented and generic programming paradigms. Stroustrup authored influential books such as The C++ Programming Language , A Tour of C++ , and The Design and Evolution of C++ . He actively contributes to the ISO C++ Standards Committee, leading efforts to evolve the language through technical papers and proposals. His research focuses on improving C++'s safety, performance, and expressiveness, including work on concepts, move semantics, and concurrency features. He has collaborated with industry and academic partners to shape the language's future direction while maintaining its efficiency and flexibility. Stroustrup's technical contributions span compiler design, template metaprogramming, and standard library development. He advocates for programming practices that balance abstraction with low-level control, making C++ a cornerstone for systems programming and high-performance applications.
Researcher
Dr. Martin Bohlen is a Research Scientist specializing in neuroimaging, optogenetics, and primate neurobiology. His work focuses on retinal ganglion cell characterization, viral vector applications in nonhuman primates, and advanced microscopy techniques for 3D imaging. Key research areas include understanding neural pathways in vision systems, developing novel imaging technologies, and studying neurodegenerative processes in viral infections. Expertise: Optogenetics, retinal cell biology, multi-camera microscopy, primate models Techniques: Lightfield imaging, viral vector analysis, single-cell omics, phototagging Recent contributions include creating single-cell atlases of rhesus macaque brains and advancing Fourier lightfield microscopy for surgical applications. His optogenetic studies explore covert attention mechanisms in primates, while viral vector research addresses gene delivery challenges in neurobiology. Lab/Team: Collaborates on multi-camera array microscopy systems and primate neuroanatomy projects.
Professor
Jeffrey S. Racine is a Professor in the Department of Economics and a Professor in the Graduate Program in Statistics in the Department of Mathematics and Statistics at McMaster University. He occupies the Senator William McMaster Chair in Econometrics and is a Fellow of the Journal of Econometrics. He serves as an Associate Editor for Econometric Reviews and as the Deputy Editor-in-Chief for Econometrics. His previous academic appointments include Syracuse University, the University of South Florida, the University of California San Diego (two-year visiting appointment), and York University. Dr. Racine earned his Ph.D. from the University of Western Ontario in 1989 under the supervision of Aman Ullah. His educational background also includes a Master's degree from McMaster University and a Bachelor of Arts (Summa Cum Laude) from McMaster University. Professor Racine's research focuses on nonparametric estimation and inference, shape constrained estimation, cross-validatory model selection, frequentist model averaging, nonparametric instrumental methods, and entropy-based measures of dependence. His work bridges theoretical econometrics with practical computational implementations, with a strong emphasis on reproducible research. He has pioneered approaches for nonparametric estimation with mixed data types (both categorical and continuous predictors) and has made significant contributions to parallel distributed computing paradigms applied to computationally intensive nonparametric estimators. His recent publications demonstrate continued innovation in model averaging techniques, kernel density estimation, and quantile regression methods. Dr. Racine has received numerous professional recognitions including the Senator William McMaster Chair in Econometrics, being named a Fellow of the Journal of Econometrics, and receiving the Econometrics Best Paper Award in 2018. His scholarly output includes multiple books, monographs, and over 100 peer-reviewed publications in leading economics and statistics journals. As an educator and researcher, Professor Racine has made substantial contributions through his co-authored graduate textbook Nonparametric Econometrics: Theory and Practice (with Qi Li, Princeton University Press, 2007) and his monograph Nonparametric Econometrics: A Primer (Foundations and Trends in Econometrics, 2008). He has also authored influential books including An Introduction to the Advanced Theory and Practice of Nonparametric Econometrics (Cambridge University Press, 2019) and Reproducible Econometrics Using R (Oxford University Press, 2019). His work on software implementation is equally impactful, having co-authored the widely used R packages np and crs available on CRAN, which have become standard tools for nonparametric econometric analysis. Professor Racine maintains an active research program with collaborators worldwide and continues to advance the field of nonparametric econometrics through both theoretical developments and practical implementations. His work has applications across economics, statistics, and various social sciences where flexible modeling approaches are required.
Professor
Professor Kimberley Goldsmith (PhD) is a Professor of Medical Statistics and Complex Intervention Methodology at the Institute of Psychiatry, Psychology & Neuroscience (IoPPN), King’s College London (KCL) . She joined KCL in 2009 after obtaining a Master of Public Health from Oregon Health & Science University and MSc in Microbiology and Virology from McMaster University . Research Focus : Mediation analysis in psychological therapies, longitudinal structural equation models, Bayesian methods, and integration of quantitative-qualitative data in implementation science. Key Projects : Sound Young Minds , TIES , SPARKLE , African Youth in Mind , and HARPdoc . Recent Publications : Notable works include cost-effectiveness of quetiapine augmentation for depression, neural biomarkers in major depressive disorder, and digital parenting interventions during the pandemic. Awards : NIHR Doctoral Research Fellowship .
Professor
Heiko Breitsohl serves as Professor and Head of the Institute for Organization, Personnel and Service Management at Alpen-Adria-Universität Klagenfurt, Austria. His academic leadership spans organizational behavior, human resources management, and industrial psychology with significant contributions to presenteeism research and methodological advancements in structural equation modeling. His research portfolio encompasses: Industrial and Organizational Psychology Human Resources Management systems Corporate Volunteering dynamics Employee Retention strategies Presenteeism and Absenteeism phenomena Structural Equation Modeling applications Survey Design and Empirical Social Research Analysis of his 15 most recent publications reveals sustained focus on workplace attendance behaviors, particularly presenteeism, with methodological rigor in scale development (e.g., Workplace Attendance Behavior Legitimacy Scale) and experimental designs. His 2021 collaborative paper in Industrial and Organizational Psychology emphasized critical synergy between substantive theory and methodological precision in organizational research. Scientific Awards: No major scientific awards listed in available sources Details regarding doctoral student supervision and research grant funding are not specified in current institutional profiles, though his leadership role implies oversight of research activities within the Institute. His ORCID and ResearchGate profiles indicate active scholarly engagement through publications and academic collaborations. As Head of Institute, Breitsohl directs research initiatives focused on organizational behavior measurement, employee well-being, and quantitative methodological innovations, maintaining strong connections with international research communities through co-authored publications with scholars across Europe and North America.
Professor
Isabel Valera is a full Professor in the Department of Computer Science at Saarland University, Saarbrücken, Germany. She also holds an adjunct faculty position at the Max Planck Institute for Software Systems (MPI-SWS) and is a fellow of the European Laboratory for Learning and Intelligent Systems (ELLIS), contributing to the Robust Machine Learning Program and the Saarbrücken AI & Machine Learning (Sam) Unit. Research Interests: Her work lies at the intersection of machine learning, fairness, and causality. She focuses on developing methods for algorithmic recourse , fair decision-making , causal modeling , and robust learning . She is particularly interested in designing models that provide actionable explanations and ensure equitable outcomes in AI systems. Her research leverages probabilistic modeling, variational inference, and deep generative architectures. Publication Trends: Her recent publications (2021–2023) show a strong emphasis on algorithmic recourse , fairness under uncertainty , causal representation learning , and multimodal and graph-based generative models . She frequently publishes in top-tier venues such as NeurIPS, ICML, ICLR, AAAI, and FAccT, often in collaboration with leading researchers like Bernhard Schölkopf and Zoubin Ghahramani. Scientific Awards and Fellowships: Humboldt Post-Doctoral Fellowship Minerva Fast Track Fellowship (Max Planck Society) ELLIS Fellow Advising and Grants: She has mentored several early-career researchers who are now active contributors in machine learning, including Adrián Javaloy and Amir-Hossein Karimi. While specific grant details are not listed, her leadership roles and fellowships indicate sustained funding support from major research organizations. She has served as a co-editor for major conferences such as AISTATS and ECML PKDD, demonstrating her active role in the academic community. Labs and Research Groups: She leads or has led research groups at both Saarland University and the Max Planck Institutes (MPI for Intelligent Systems and MPI for Software Systems), focusing on foundational and applied aspects of machine learning with societal impact.
Associate Professor
Andreas Kryger Jensen is an Associate Professor in the Department of Public Health at the University of Copenhagen, Faculty of Health and Medical Sciences. His academic work is centered in the Section of Biostatistics, where he contributes to methodological and applied research in biostatistics and public health. He is actively engaged in research output, peer review, and academic collaboration. His primary research interests include: Functional Data Analysis Bioinformatics Design and analysis of clinical trials Manifold statistics Machine learning applications in health Environmental and pediatric epidemiology His recent publications demonstrate a strong focus on developing and applying advanced statistical methods to complex health datasets. Articles span topics such as deep learning for functional data alignment, air pollution mixtures and childhood asthma, hormonal profiles in pediatric diabetes, and predictive modeling in maternal and child health. These works appear in high-quality journals like Statistics and Computing , PLoS ONE , Environmetrics , and JACC: Advances , indicating interdisciplinary reach and methodological rigor. His research trends reflect a commitment to solving public health challenges through innovative statistical modeling, particularly in longitudinal and functional data contexts. He integrates machine learning with traditional biostatistical methods to improve prediction, causal inference, and data interpretation in clinical and population health settings. While no specific scientific awards are mentioned in the provided text, his sustained publication record, editorial roles, and active research profile suggest recognition within the academic community. Andreas Kryger Jensen is involved in academic service, including peer review for journals such as Biostatistics , Scandinavian Journal of Statistics , and Biometrical Journal , as well as participation in conferences and outreach lectures. He has also served on external committees, including at Statens Serum Institut, indicating broader institutional engagement. Though no specific students are named, his role as an associate professor and active researcher implies involvement in mentoring graduate students and early-career researchers in biostatistics and public health. His work is supported by collaborative networks across Denmark and internationally, particularly in environmental health and clinical research.
Professor
Jonathan McGavock is a Professor in the Department of Pediatrics & Child Health at the University of Manitoba and an Investigator at the Children's Hospital Research Institute of Manitoba (CHRI). He leads the Physical Activity, Cardiometabolic health & Equity (PACE) Lab, which focuses on preventing and managing type 2 diabetes in youth through physical activity, behavioral interventions, and health equity initiatives. His research spans clinical trials, implementation science, and community-engaged studies with a strong commitment to Indigenous health and anti-racist practices. His research interests center on pediatric cardiometabolic health, with a focus on type 1 and type 2 diabetes, physical activity, and emotional regulation. He investigates how urban infrastructure like trails impacts youth health and how behavioral interventions can improve outcomes in at-risk adolescents. His work emphasizes health equity, particularly for Indigenous communities, and integrates Truth and Reconciliation principles through partnerships with Elders, knowledge keepers, and patient co-researchers. His recent research projects reflect a strong trend in interdisciplinary, patient-oriented science, combining clinical trials (e.g., TEAM Trial, DIRECTION Trial) with population-level studies on urban design and Indigenous health. These studies often employ causal methods and implementation frameworks to inform policy and practice. His work is funded by major agencies including CIHR, JDRF, and Diabetes Canada, reflecting national recognition of his contributions. Scientific awards and honors are not explicitly listed, but his leadership of large, multi-year grants indicates significant peer recognition. His research is supported by major funding programs such as Diabetes Action Canada 2.0 and the CIHR Health Research Training Platform Grant (my ROaD), which also support trainee development. Dr. McGavock mentors graduate students and leads training initiatives focused on obesity and diabetes research. His lab, the PACE Lab, fosters an environment of equity, anti-racism, and community partnership. He is involved in advising through structured mentorship programs and graduate supervision, though specific student names are not listed. His research includes major health system interventions, such as Ikiskawasoot for Indigenous maternal health and the Indigenous Youth Mentorship Program. The PACE Lab operates on Treaty 1 Territory and conducts research across Treaties 2, 5, and adhesion to Treaty 5, emphasizing collaboration with Anishinaabeg, Nehethowuk (Ininew), Red River Métis, and other Indigenous communities. The lab is committed to decolonizing research practices, supporting Indigenous hiring, and integrating traditional healing into health interventions.
Professor
Martin Singull is a Professor and Head of Division in Applied Mathematics at the Department of Mathematics, Faculty of Science and Engineering, Linköping University. He has held academic positions at Linköping University since 2012, progressing from Assistant Professor to full Professor in 2020, and serving in leadership roles since 2016. His research is centered on mathematical statistics, particularly statistical inference for complex, high-dimensional data with repeated measurements. His research interests include: Statistical inference for repeated measurements and growth curve models Classification and discriminant analysis Multivariate statistical analysis High-dimensional data modeling Applications in public health, cardio-oncology, and development contexts Martin Singull's recent publications focus on likelihood-based classification, Edgeworth-type expansions for distribution approximation, residual analysis in GMANOVA-MANOVA models, and the estimation of misclassification probabilities. His work often involves collaboration with Dietrich von Rosen and other researchers, applying advanced statistical techniques to both theoretical and applied problems. A strong trend in his research is the development of efficient classifiers using temporal and spatial information in longitudinal data. His scientific service includes: Director for the Research School in Interdisciplinary Mathematics (2024–) Chair of the organizing committee for IWMS 2023 and LinStat2014 Team Leader for Sida-funded bilateral programs in mathematics with universities in Africa Member of the ISP Mathematics Reference Group Board member of the Faculty of Science and Engineering Martin Singull has supervised numerous PhD students, both at Linköping University and in collaborative programs in Rwanda, Uganda, Tanzania, Mozambique, and Cambodia. His advising spans theoretical statistics and applied interdisciplinary research. He leads research collaborations aimed at strengthening mathematical capacity in low-income countries, particularly in Africa. His work is supported by funding from Sida and ISP, and he is actively involved in international academic networks such as the International Workshop on Matrices and Statistics (IWMS). He is also associated with research initiatives in computational cardio-oncology, where statistical models are used to predict cardiovascular complications in cancer survivors, and has contributed to projects on fistula prevention and health outcomes in low-resource settings.
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